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Galter, I. ; Schneltzer, E. ; Marr, C. ; Spielmann, N. ; Hrabě de Angelis, M.

EchoVisuAL: Efficient Segmentation of Echocardiograms Using Deep Active Learning.

In: (Medical Image Understanding and Analysis). Berlin [u.a.]: Springer, 2024. 366-381 (Lect. Notes Comput. Sc. ; 14860 LNCS)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Echocardiography is a fast and cost-effective imaging technique for assessing cardiac function and structure. However, image-derived phenotypic evaluation is challenging. Current AI-approaches designed for automatic interpretation of echocardiography data are progressing, but algorithms for animal models frequently used in pre-clinical studies are rare. Here, we propose a deep active learning approach, called EchoVisuAL, that uses large-scale, multi-center data of the International Mouse Phenotyping Consortium (IMPC). This heterogeneous IMPC data set includes 96 392 echocardiograms with 3 831 290 frames from 17 991 mice. Heterogeneity is characterized by differences in age, sex, background strains, anesthesia, imaging frequency and focus depth. EchoVisuAL is founded on a Bayesian U-Net that produces inner trace segmentations alongside with two confidence metrics, an uncertainty measure and a BALD score. This architecture, embedded in an active learning framework, enables a substantial reduction of the annotation efforts by an intelligent selection of the next frames that should be annotated. In total, 15 models were trained on step-wise increasing training data sets based on the model’s confidence. For model evaluation, 25 echocardiograms with 1062 frames were annotated by four highly experienced, independent experts. Inter-rater-agreement across all frames was high with a mean Randolph’s kappa score of 0.91±0.10. Across models, high Dice scores were observed on these expert annotations, currently considered as the gold standard, with model M15 achieving a mean Dice score of 0.98±0.02. EchoVisuAL is a new deep active learning application robust to automatically analyze heterogeneous mouse echocardiograms, including uncertainty scores for user guidance.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Bayesian Neural Networks ; Cardiac Segmentation ; Deep Active Learning ; Echocardiography
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title Medical Image Understanding and Analysis
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 14860 LNCS, Issue: , Pages: 366-381 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Non-patent literature Publications
Institute(s) Institute of Experimental Genetics (IEG)
Institute of AI for Health (AIH)